Type 2 diabetes mellitus (T2DM) and related disorders are a major health problem and rapidly increasing in prevalence in most human populations. We have identified a chromosomal location (6q23) which is strongly linked to "diabesity" phenotypes in Mexican Americans. The phenotypes include fasting plasma insulin and obesity. These findings have recently been independently replicated in several other populations. We propose to localize and identify the susceptibility gene or genes located at 6q23 in a large Mexican American cohort comprising > 2,000 subjects from two large population studies (SAFADS/SAFGDS and VAGES). This is the largest repository of Mexican American DNAs and phenotypic data for diabetes gene discovery and provides unprecedented power to localize genes. Genome-wide scans are complete in SAFADS/SAFGDS and will soon be completed in VAGES. We will select single nucleotide polymorphisms (SNPs), from public and private SNP databases, which cover the linked region at high density (1 per 10 kb). The SNPs will be located gene-centrically within the linkage peak region responsible for > 99% of the evidence for linkage. These SNPs will be genotyped using high-throughput procedures, and the resulting data will be used to measure linkage disequilibrium and define haplotype structure within the region. SNP genotyping will be performed in a novel cost-minimizing three-phase strategy using progressively larger datasets. We will calculate association of SNPs with diabesity phenotypes and perform linkage analysis conditional on associated SNPs, to define loci which contain variants that account for the observed linkage. All common gene-centric SNPs within a defined target gene or genes will be genotyped and the data analyzed using a novel quantitative trait nucleotide (QTN) approach which utilizes the information from all SNPs simultaneously to fully account for association. Rigorous validation of the highest probability gene(s) will utilize a battery of statistical and molecular genetic techniques: (i) QTN and conditional linkage analyses; (ii) bioinformatic analysis and prediction of gene/mRNA/protein structure, function, and cellular location; and (iii) expression analyses of normal and variant forms of the gene using DNA microarrays and quantitative RT-PCR, in appropriate cell lines, under various nutritional and hormonal perturbations, to elucidate pathophysiological mechanisms, prior to the design of complementary in vivo clinical studies.